Few-shot learning. With increased use of encoder-decoder models, methods have also been
proposed to efficiently augment the training with a small number of
human-generated summaries (50 to 100). [1]} train
transformer models on a small number of examples and during inference,
generate multiple summaries which are later ranked according to coherence
to arrive at a final one. Other approaches focus on
an additional plug-in network that can predict desired properties of
summaries based on a few labelled examples [2]} that can
augment training signals. [3]}
introduce the use of a few additional parameters in the form of adaptors
and only these are finetuned instead of the full network, making the training
efficient and robust for few-shot learning.
